diff --git a/mhcflurry/class1_presentation_predictor.py b/mhcflurry/class1_presentation_predictor.py
index a1f67ee80a38dae975911c65af70387c59b1bda6..bdb397059419cb02a53daa41e1b807eb560da834 100644
--- a/mhcflurry/class1_presentation_predictor.py
+++ b/mhcflurry/class1_presentation_predictor.py
@@ -89,7 +89,8 @@ class Class1PresentationPredictor(object):
verbose=1,
throw=True):
"""
- Predict binding affinities.
+ Predict binding affinities across samples (each corresponding to up to
+ six MHC I alleles).
Two modes are supported: each peptide can be evaluated for binding to
any of the alleles in any sample (this is what happens when sample_names
@@ -97,7 +98,8 @@ class Class1PresentationPredictor(object):
of the sample given by the i'th entry in sample_names.
For example, if we don't specify sample_names, then predictions
- are taken for all combinations of samples and peptides:
+ are taken for all combinations of samples and peptides, for a result
+ size of num peptides * num samples:
>>> predictor = Class1PresentationPredictor.load()
>>> predictor.predict_affinity(
@@ -114,7 +116,8 @@ class Class1PresentationPredictor(object):
3 PEPTIDE 1 sample2 34362.109211 C0202
In contrast, here we specify sample_names, so peptide is evaluated for
- binding the alleles in the corresponding sample:
+ binding the alleles in the corresponding sample, for a result size equal
+ to the number of peptides:
>>> predictor.predict_affinity(
... peptides=["SIINFEKL", "PEPTIDE"],
@@ -132,14 +135,14 @@ class Class1PresentationPredictor(object):
Parameters
----------
peptides : list of string
+ Peptide sequences
alleles : dict of string -> list of string
Keys are sample names, values are the alleles (genotype) for
that sample
sample_names : list of string [same length as peptides]
- Sample names corresponding to each peptide. These are used to
- lookup the alleles for each peptide in the alleles dict. If not
- specified, then predictions are generated for all sample genotypes
- across all peptides.
+ Sample names corresponding to each peptide. If None, then
+ predictions are generated for all sample genotypes across all
+ peptides.
include_affinity_percentile : bool
Whether to include affinity percentile ranks
verbose : int
@@ -469,11 +472,33 @@ class Class1PresentationPredictor(object):
the binding affinity and processing predictions and other intermediate
results.
+ Example:
+
+ >>> predictor = Class1PresentationPredictor.load()
+ >>> predictor.predict_to_dataframe(
+ ... peptides=["SIINFEKL", "PEPTIDE"],
+ ... n_flanks=["NNN", "SNS"],
+ ... c_flanks=["CCC", "CNC"],
+ ... alleles={
+ ... "sample1": ["A0201", "A0301", "B0702"],
+ ... "sample2": ["A0101", "C0202"],
+ ... },
+ ... verbose=0)
+ peptide n_flank c_flank peptide_num sample_name affinity best_allele processing_score presentation_score
+ 0 SIINFEKL NNN CCC 0 sample1 12906.787792 A0201 0.802466 0.140365
+ 1 PEPTIDE SNS CNC 1 sample1 36827.681130 B0702 0.105260 0.004059
+ 2 SIINFEKL NNN CCC 0 sample2 3588.413748 C0202 0.802466 0.338647
+ 3 PEPTIDE SNS CNC 1 sample2 34362.109211 C0202 0.105260 0.004317
+
+ You can also specify sample_names, in which case peptide is evaluated
+ for binding the alleles in the corresponding sample only. See
+ `predict_affinity` for an examples.
+
Parameters
----------
peptides : list of string
Peptide sequences
- alleles : list of string or string -> list of string dict
+ alleles : list of string or dict of string -> list of string
If you are predicting for a single sample, pass a list of strings
(up to 6) indicating the genotype. If you are predicting across
multiple samples, pass a dict where the keys are (arbitrary)
@@ -481,9 +506,8 @@ class Class1PresentationPredictor(object):
sample.
sample_names : list of string [same length as peptides]
If you are passing a dict for 'alleles', you can use this
- argument to
- specify which peptides go with which samples. If it is None,
- then predictions will be performed for each peptide across all
+ argument to specify which peptides go with which samples. If it is
+ None, then predictions will be performed for each peptide across all
samples.
n_flanks : list of string [same length as peptides]
Upstream sequences before the peptide. Sequences of any length can
@@ -584,6 +608,34 @@ class Class1PresentationPredictor(object):
"""
Predict presentation across protein sequences.
+ Example:
+
+ >>> predictor = Class1PresentationPredictor.load()
+ >>> predictor.predict_sequences(
+ ... sequences={
+ ... 'protein1': "MDSKGSSQKGSRLLLLLVVSNLL",
+ ... 'protein2': "SSLPTPEDKEQAQQTHH",
+ ... },
+ ... alleles={
+ ... "sample1": ["A0201", "A0301", "B0702"],
+ ... "sample2": ["A0101", "C0202"],
+ ... },
+ ... result="filtered",
+ ... comparison_quantity="affinity",
+ ... filter_value=500,
+ ... verbose=0)
+ sequence_name pos peptide n_flank c_flank sample_name affinity best_allele affinity_percentile processing_score presentation_score
+ 0 protein1 13 LLLLVVSNL MDSKGSSQKGSRL L sample1 38.206225 A0201 0.380125 0.017644 0.571060
+ 1 protein1 14 LLLVVSNLL MDSKGSSQKGSRLL sample1 42.243472 A0201 0.420250 0.090984 0.619213
+ 2 protein1 5 SSQKGSRLL MDSKG LLLVVSNLL sample2 66.749223 C0202 0.803375 0.383608 0.774468
+ 3 protein1 6 SQKGSRLLL MDSKGS LLVVSNLL sample2 178.033474 C0202 1.820000 0.275019 0.482206
+ 4 protein1 13 LLLLVVSNLL MDSKGSSQKGSRL sample1 202.208167 A0201 1.112500 0.058782 0.261320
+ 5 protein1 12 LLLLLVVSNL MDSKGSSQKGSR L sample1 202.506582 A0201 1.112500 0.010025 0.225648
+ 6 protein2 0 SSLPTPEDK EQAQQTHH sample1 335.529377 A0301 1.011750 0.010443 0.156798
+ 7 protein2 0 SSLPTPEDK EQAQQTHH sample2 353.451759 C0202 2.674250 0.010443 0.150753
+ 8 protein1 8 KGSRLLLLL MDSKGSSQ VVSNLL sample2 410.327286 C0202 2.887000 0.121374 0.194081
+ 9 protein1 5 SSQKGSRL MDSKG LLLLVVSNLL sample2 477.285954 C0202 3.107375 0.111982 0.168572
+
Parameters
----------
sequences : str, list of string, or string -> string dict
@@ -665,7 +717,7 @@ class Class1PresentationPredictor(object):
if all(isinstance(a, string_types) for a in alleles):
# Case (2) - a simple list of alleles
alleles = {
- 'genotype': alleles
+ 'sample1': alleles
}
else:
# Case (3) - a list of lists
@@ -730,7 +782,7 @@ class Class1PresentationPredictor(object):
result_df.peptide_num.map(pandas.Series(sequence_names)))
result_df.insert(
1,
- "position_in_sequence",
+ "pos",
result_df.peptide_num.map(pandas.Series(position_in_sequence)))
del result_df["peptide_num"]
diff --git a/test/test_doctest.py b/test/test_doctest.py
index 9b9205a60c841c1bb1da77441b5bd1c0701285e6..aba376396bdce059bf118bf46f6403134deb3347 100644
--- a/test/test_doctest.py
+++ b/test/test_doctest.py
@@ -1,6 +1,9 @@
"""
Run doctests.
"""
+import logging
+logging.getLogger('matplotlib').disabled = True
+logging.getLogger('tensorflow').disabled = True
import os
import doctest